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Everything about Differential dynamic programming

Algorithm for trajectory optimization

Differential dynamic programming (DDP) is an optimal control algorithm of the trajectory optimization class. The algorithm was introduced in 1966 by Mayne and subsequently analysed in Jacobson and Mayne's eponymous book. The algorithm uses locally-quadratic models of the dynamics and cost functions, and displays quadratic convergence. It is closely related to Pantoja's step-wise Newton's method. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Differential dynamic programmingA 5-minute orientation
  2. Skim a recent paper: Safety Embedded Differential Dynamic Programming Using Discrete Barrier StatesInfluential work since 2022
  3. Pick up a classic book: Methods of qualitative theory in nonlinear dynamicsThe most-reprinted book on the subject
  4. Read the landmark paper: Hydroelectric generation scheduling with an effective differential dynamic programming algorithmCited 102 times
  5. Go sideways: Model predictive controlA closely related topic
  6. Go sideways: Linear–quadratic–Gaussian controlA closely related topic

đź“– Definitions

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🗂️ The subject at a glance

  1. Finite-horizon discrete-time problems
  2. Dynamic programming
  3. Differential dynamic programming
  4. Regularization and line-search
  5. Monte Carlo version
  6. Constrained problems

🏆 Landmark papers (most cited)

🆕 Influential since 2022

đź“š Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

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